Modelling brain representations of abstract concepts

Kaiser, Daniel; Jacobs, Arthur M.; Cichy, Radoslaw Martin

doi:10.1371/journal.pcbi.1009837

Cited by 6 publications

(7 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our findings support the previously described encoding-retrieval distinction and suggest that it is primarily conceptual information (Fig. 2b) that is represented in parietal regions during retrieval, in line with previous work showing strong object identity - and even abstract concept coding in parietal cortex (Ferreira et al, 2019; Jeong & Xu, 2016; Kaiser et al, 2022). The involvement of parietal networks is also discussed further below in relation to the timing of the reactivation.…”

Section: Discussionsupporting

confidence: 93%

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Lifanov

Griffiths

Linde-Domingo

et al. 2022

Preprint

View full text Add to dashboard Cite

Our understanding of how information unfolds when we recall events from memory remains limited. In this study, we investigate whether the reconstruction of visual object memories follows a backward trajectory along the ventral visual stream with respect to perception, such that their neural feature representations are gradually reinstated from late areas close to the hippocampus backwards to lower-level sensory areas. We use multivariate analyses of fMRI activation patterns to map the constituent features of the object memories onto the brain during retrieval, and EEG-fMRI fusion to track the temporal evolution of the reactivated patterns. Participants studied new associations between verbs and randomly paired object images in an encoding phase, and subsequently recalled the objects when presented with the corresponding verb cue. Decoding reactivated memory features from fMRI activity revealed that retrieval patterns were dominated by conceptual features, represented in comparatively late visual and parietal areas. Representational-similarity-based fusion then allowed us to map the EEG patterns that emerged at each given time point of a trial onto the spatially resolved fMRI patterns. This fusion suggests that memory reconstruction proceeds backwards along the ventral visual stream from anterior fronto-temporal to posterior occipital and parietal regions, in line with a semantic-to-perceptual gradient. A linear regression on the peak time points of reactivated brain regions statistically confirms that the temporal progression is reversed with respect to encoding. Together, the results shed light onto the spatio-temporal trajectories along which memories are reconstructed during associative retrieval, and which features of an image are reconstructed when in time and where in the brain.

show abstract

Section: Discussionsupporting

confidence: 93%

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Lifanov

Griffiths

Linde-Domingo

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, this approach is relevant in work using semantic similarity metrics to identify brain networks which support different representations (e.g., abstract and concrete concepts, taxonomic and thematic concepts, nouns and verbs; cf. Akinina et al, 2019; Kaiser et al, 2022; Schwartz et al, 2009, 2011; Wang et al, 2018), to categorize neurological disease (e.g., primary progressive aphasia variants, poststroke aphasia, cf. Budd et al, 2010; Jefferies & Lambon Ralph, 2006; Reilly et al, 2011), to identify the level of naming deficits (e.g., Caramazza, 1997; Dell et al, 1997; Walker et al, 2018), and to understand the efficacy of language treatment (e.g., Kristinsson et al, 2021; Leonard et al, 2015; Miceli et al, 1996; Nickels & Best, 1996).…”

Section: Discussionmentioning

confidence: 99%

Assessing naming errors using an automated machine learning approach.

Schnur¹,

Lei²

2022

Neuropsychology

View full text Add to dashboard Cite

Objective: After left hemisphere stroke, 20%–50% of people experience language deficits, including difficulties in naming. Naming errors that are semantically related to the intended target (e.g., producing “violin” for picture HARP) indicate a potential impairment in accessing knowledge of word forms and their meanings. Understanding the cause of naming impairments is crucial to better modeling of language production as well as for tailoring individualized rehabilitation. However, evaluation of naming errors is typically by subjective and laborious dichotomous classification. As a result, these evaluations do not capture the degree of semantic similarity and are susceptible to lower interrater reliability because of subjectivity. Method: We investigated whether a computational linguistic measure using word2vec (Mikolov, Chen, et al., 2013) addressed these limitations by evaluating errors during object naming in a group of patients during the acute stage of a left-hemisphere stroke (N = 105). Results: Pearson correlations demonstrated excellent convergent validity of word2vec’s semantically related estimates of naming errors and independent tests of access to lexical–semantic knowledge (p < .0001). Further, multiple regression analysis showed word2vec’s semantically related estimates were significantly better than human error classification at predicting performance on tests of lexical–semantic knowledge. Conclusions: Useful to both theorists and clinicians, our word2vec-based method provides an automated, continuous, and objective psychometric measure of access to lexical–semantic knowledge during naming.

show abstract

“…This form often involves dynamically imagining and articulating structured narratives, such as delivering a speech. Situationally simulated inner speech involved (1) a process of inner speech, (2) the integration of contextual background, episodic and semantic memories, past experiences, and upcoming events to construct contextually coherent narratives (Andrews, 2016(Andrews, , 2014Kaiser et al, 2022), and (3) the representation of complex and dynamic information, contrasting with simple imagining of vowels, consonants, tones, and words. Despite extensive research on the neural mechanisms of inner speech more generally (Alderson-Day & Fernyhough, 2015;Fernyhough & Borghi, 2023;Grandchamp et al, 2019;Perrone-Bertolotti et al, 2014 ), the neural mechanisms underlying situationally simulated inner speech remain largely unknown.…”

Section: Introductionmentioning

confidence: 99%

“…To validate these hypotheses, we conducted dynamic activation and connectivity analyses on an fMRI dataset (Kaiser et al, 2022) comprising 10 sessions of situationally simulated inner speech. In each session, participants were presented with 61 abstract words and instructed to mentally incorporate them into a cohesive narrative within a predefined contextual backdrop.…”

Section: Introductionmentioning

confidence: 99%

How do we imagine a speech? A triple network model for situationally simulated inner speech

Gao,

Yang,

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Inner speech is a silent verbal experience and plays central roles in human consciousness and cognition. Despite impressive studies over the past decades, the neural mechanisms of inner speech remain largely unknown. In this study, we adopted an ecological paradigm called situationally simulated inner speech. Unlike mere imaging speech of words, situationally simulated inner speech involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure. We conducted dynamic activation and network analyses on fMRI data, where participants were instructed to engage in inner speech prompted by cue words across 10 different contextual backgrounds. Our seed-based co-activation pattern analyses revealed dynamic involvement of the language network, sensorimotor network, and default mode network in situationally simulated inner speech. Additionally, frame-wise dynamic conditional correlation analysis uncovered four temporal-reoccurring states with distinct functional connectivity patterns among these networks. We proposed a triple network model for deliberate inner speech, including language network for a truncated form of overt speech, sensorimotor network for perceptual simulation and monitoring, and default model network for integration and ‘sense-making’ processing.HighlightsIn ten contextual backgrounds, subjects were instructed to perform situationally simulated inner speech based on cue words.The ventral parts of the bilateral somatosensory areas and middle superior temporal gyrus were as centers for seed-based co-activation pattern analyses.A triple network model of language network, sensorimotor network, and default mode network was proposed for deliberate inner speech.

show abstract

Modelling brain representations of abstract concepts

Cited by 6 publications

References 72 publications

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Assessing naming errors using an automated machine learning approach.

How do we imagine a speech? A triple network model for situationally simulated inner speech

Contact Info

Product

Resources

About